Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations197
Missing cells2
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory176.1 KiB
Average record size in memory915.3 B

Variable types

Text2
DateTime2
Categorical14
Numeric8

Alerts

cluster_k5 has constant value "2" Constant
antiguedad is highly overall correlated with periodo_preinscripcionHigh correlation
cant_Apoderado is highly overall correlated with cant_sinMontoLimiteHigh correlation
cant_antecedentes is highly overall correlated with cant_suspensionesHigh correlation
cant_autenticado is highly overall correlated with cant_sinMontoLimiteHigh correlation
cant_noAutenticado is highly overall correlated with cant_sinMontoLimiteHigh correlation
cant_procesos_adjudicado is highly overall correlated with monto_total_adjudicadoHigh correlation
cant_representante is highly overall correlated with dmonto_total_adjudicadoHigh correlation
cant_sinMontoLimite is highly overall correlated with cant_Apoderado and 2 other fieldsHigh correlation
cant_suspensiones is highly overall correlated with cant_antecedentesHigh correlation
dmonto_total_adjudicado is highly overall correlated with cant_representanteHigh correlation
monto_total_adjudicado is highly overall correlated with cant_procesos_adjudicadoHigh correlation
periodo_preinscripcion is highly overall correlated with antiguedadHigh correlation
Estado is highly imbalanced (70.9%) Imbalance
provincia is highly imbalanced (51.5%) Imbalance
cant_autenticado is highly imbalanced (66.0%) Imbalance
cant_noAutenticado is highly imbalanced (56.4%) Imbalance
cant_MontoLimite is highly imbalanced (86.3%) Imbalance
CUIT has unique values Unique
monto_total_adjudicado has unique values Unique
cant_socios has 45 (22.8%) zeros Zeros
cant_suspensiones has 107 (54.3%) zeros Zeros
cant_antecedentes has 3 (1.5%) zeros Zeros

Reproduction

Analysis started2025-07-08 14:19:22.087184
Analysis finished2025-07-08 14:19:28.164292
Duration6.08 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

CUIT
Text

Unique 

Distinct197
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
2025-07-08T11:19:28.262954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.994924
Min length9

Characters and Unicode

Total characters2166
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique197 ?
Unique (%)100.0%

Sample

1st row30711500363
2nd row30590151013
3rd row30678561165
4th row30591267759
5th row30702024834
ValueCountFrequency (%)
30711500363 1
 
0.5%
30590151013 1
 
0.5%
30678561165 1
 
0.5%
30591267759 1
 
0.5%
30702024834 1
 
0.5%
30623295946 1
 
0.5%
30673249902 1
 
0.5%
30714236888 1
 
0.5%
30710362218 1
 
0.5%
30710828608 1
 
0.5%
Other values (187) 187
94.9%
2025-07-08T11:19:28.435469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 356
16.4%
3 304
14.0%
7 249
11.5%
2 238
11.0%
1 199
9.2%
6 196
9.0%
9 171
7.9%
8 162
7.5%
5 155
7.2%
4 135
 
6.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2166
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 356
16.4%
3 304
14.0%
7 249
11.5%
2 238
11.0%
1 199
9.2%
6 196
9.0%
9 171
7.9%
8 162
7.5%
5 155
7.2%
4 135
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2166
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 356
16.4%
3 304
14.0%
7 249
11.5%
2 238
11.0%
1 199
9.2%
6 196
9.0%
9 171
7.9%
8 162
7.5%
5 155
7.2%
4 135
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2166
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 356
16.4%
3 304
14.0%
7 249
11.5%
2 238
11.0%
1 199
9.2%
6 196
9.0%
9 171
7.9%
8 162
7.5%
5 155
7.2%
4 135
 
6.2%

Nombre
Text

Distinct192
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Memory size16.4 KiB
2025-07-08T11:19:28.576719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length67
Median length34
Mean length18.558376
Min length3

Characters and Unicode

Total characters3656
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique191 ?
Unique (%)97.0%

Sample

1st rowLICICOM S.R.L.
2nd rowVIDITEC S.A..
3rd rowNACION SEGUROS S.A.
4th rowERRE-DE SRL
5th rowDatastar Argentina S.A.
ValueCountFrequency (%)
s.a 57
 
10.1%
srl 36
 
6.4%
s.r.l 21
 
3.7%
sa 20
 
3.6%
argentina 13
 
2.3%
y 11
 
2.0%
de 10
 
1.8%
datos 6
 
1.1%
sin 6
 
1.1%
s 5
 
0.9%
Other values (338) 377
67.1%
2025-07-08T11:19:28.826744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
365
 
10.0%
A 289
 
7.9%
S 267
 
7.3%
R 227
 
6.2%
E 199
 
5.4%
I 198
 
5.4%
. 186
 
5.1%
N 145
 
4.0%
O 140
 
3.8%
L 135
 
3.7%
Other values (54) 1505
41.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3656
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
365
 
10.0%
A 289
 
7.9%
S 267
 
7.3%
R 227
 
6.2%
E 199
 
5.4%
I 198
 
5.4%
. 186
 
5.1%
N 145
 
4.0%
O 140
 
3.8%
L 135
 
3.7%
Other values (54) 1505
41.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3656
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
365
 
10.0%
A 289
 
7.9%
S 267
 
7.3%
R 227
 
6.2%
E 199
 
5.4%
I 198
 
5.4%
. 186
 
5.1%
N 145
 
4.0%
O 140
 
3.8%
L 135
 
3.7%
Other values (54) 1505
41.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3656
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
365
 
10.0%
A 289
 
7.9%
S 267
 
7.3%
R 227
 
6.2%
E 199
 
5.4%
I 198
 
5.4%
. 186
 
5.1%
N 145
 
4.0%
O 140
 
3.8%
L 135
 
3.7%
Other values (54) 1505
41.2%
Distinct154
Distinct (%)78.2%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Minimum2016-02-08 00:00:00
Maximum2020-10-16 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-08T11:19:28.905588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:29.014954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Estado
Categorical

Imbalance 

Distinct8
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
Inscripto
172 
Desactualizado Por Documentos Vencidos
 
7
Suspendido
 
5
Con Solicitud De Baja
 
4
Desactualizado Por Mantencion Formulario
 
4
Other values (3)
 
5

Length

Max length40
Median length9
Mean length11.086294
Min length9

Characters and Unicode

Total characters2184
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowInscripto
2nd rowInscripto
3rd rowInscripto
4th rowInscripto
5th rowInscripto

Common Values

ValueCountFrequency (%)
Inscripto 172
87.3%
Desactualizado Por Documentos Vencidos 7
 
3.6%
Suspendido 5
 
2.5%
Con Solicitud De Baja 4
 
2.0%
Desactualizado Por Mantencion Formulario 4
 
2.0%
Pre Inscripto 3
 
1.5%
En Evaluacion 1
 
0.5%
Desactualizado Por Clase 1
 
0.5%

Length

2025-07-08T11:19:29.108692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:29.185891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
inscripto 175
70.6%
desactualizado 12
 
4.8%
por 12
 
4.8%
documentos 7
 
2.8%
vencidos 7
 
2.8%
suspendido 5
 
2.0%
con 4
 
1.6%
solicitud 4
 
1.6%
de 4
 
1.6%
baja 4
 
1.6%
Other values (6) 14
 
5.6%

Most occurring characters

ValueCountFrequency (%)
o 246
11.3%
i 216
9.9%
n 212
9.7%
c 210
9.6%
s 207
9.5%
t 202
9.2%
r 198
9.1%
p 180
8.2%
I 175
8.0%
a 55
 
2.5%
Other values (18) 283
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2184
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 246
11.3%
i 216
9.9%
n 212
9.7%
c 210
9.6%
s 207
9.5%
t 202
9.2%
r 198
9.1%
p 180
8.2%
I 175
8.0%
a 55
 
2.5%
Other values (18) 283
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2184
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 246
11.3%
i 216
9.9%
n 212
9.7%
c 210
9.6%
s 207
9.5%
t 202
9.2%
r 198
9.1%
p 180
8.2%
I 175
8.0%
a 55
 
2.5%
Other values (18) 283
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2184
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 246
11.3%
i 216
9.9%
n 212
9.7%
c 210
9.6%
s 207
9.5%
t 202
9.2%
r 198
9.1%
p 180
8.2%
I 175
8.0%
a 55
 
2.5%
Other values (18) 283
13.0%

TipoSocietario
Categorical

Distinct7
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size21.7 KiB
Sociedad Anónima
80 
Sociedad Responsabilidad Limitada
60 
Persona Física
44 
Otras Formas Societarias
 
7
Sociedades De Hecho
 
3
Other values (2)
 
3

Length

Max length40
Median length33
Mean length21.309645
Min length14

Characters and Unicode

Total characters4198
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowSociedad Responsabilidad Limitada
2nd rowSociedad Anónima
3rd rowSociedad Anónima
4th rowSociedad Responsabilidad Limitada
5th rowSociedad Anónima

Common Values

ValueCountFrequency (%)
Sociedad Anónima 80
40.6%
Sociedad Responsabilidad Limitada 60
30.5%
Persona Física 44
22.3%
Otras Formas Societarias 7
 
3.6%
Sociedades De Hecho 3
 
1.5%
Persona Jurídica Extranjero Sin Sucursal 2
 
1.0%
Organismo Publico 1
 
0.5%

Length

2025-07-08T11:19:29.275516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:29.338008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sociedad 140
29.8%
anónima 80
17.0%
responsabilidad 60
12.8%
limitada 60
12.8%
persona 46
 
9.8%
física 44
 
9.4%
otras 7
 
1.5%
formas 7
 
1.5%
societarias 7
 
1.5%
sociedades 3
 
0.6%
Other values (8) 16
 
3.4%

Most occurring characters

ValueCountFrequency (%)
a 588
14.0%
i 527
12.6%
d 468
11.1%
273
 
6.5%
n 271
 
6.5%
o 270
 
6.4%
e 267
 
6.4%
s 237
 
5.6%
c 202
 
4.8%
S 154
 
3.7%
Other values (23) 941
22.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4198
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 588
14.0%
i 527
12.6%
d 468
11.1%
273
 
6.5%
n 271
 
6.5%
o 270
 
6.4%
e 267
 
6.4%
s 237
 
5.6%
c 202
 
4.8%
S 154
 
3.7%
Other values (23) 941
22.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4198
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 588
14.0%
i 527
12.6%
d 468
11.1%
273
 
6.5%
n 271
 
6.5%
o 270
 
6.4%
e 267
 
6.4%
s 237
 
5.6%
c 202
 
4.8%
S 154
 
3.7%
Other values (23) 941
22.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4198
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 588
14.0%
i 527
12.6%
d 468
11.1%
273
 
6.5%
n 271
 
6.5%
o 270
 
6.4%
e 267
 
6.4%
s 237
 
5.6%
c 202
 
4.8%
S 154
 
3.7%
Other values (23) 941
22.4%

periodo_preinscripcion
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201688.73
Minimum201607
Maximum202010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-07-08T11:19:29.431956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum201607
5-th percentile201608
Q1201611
median201701
Q3201706
95-th percentile201902.4
Maximum202010
Range403
Interquartile range (IQR)95

Descriptive statistics

Standard deviation88.146583
Coefficient of variation (CV)0.00043704268
Kurtosis1.8652576
Mean201688.73
Median Absolute Deviation (MAD)90
Skewness1.3240382
Sum39732680
Variance7769.8201
MonotonicityNot monotonic
2025-07-08T11:19:29.526569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
201611 23
 
11.7%
201701 23
 
11.7%
201610 20
 
10.2%
201612 15
 
7.6%
201703 13
 
6.6%
201609 12
 
6.1%
201702 10
 
5.1%
201608 9
 
4.6%
201704 9
 
4.6%
201706 5
 
2.5%
Other values (30) 58
29.4%
ValueCountFrequency (%)
201607 4
 
2.0%
201608 9
 
4.6%
201609 12
6.1%
201610 20
10.2%
201611 23
11.7%
201612 15
7.6%
201701 23
11.7%
201702 10
5.1%
201703 13
6.6%
201704 9
 
4.6%
ValueCountFrequency (%)
202010 1
0.5%
202009 1
0.5%
202001 1
0.5%
201911 1
0.5%
201910 2
1.0%
201908 1
0.5%
201907 1
0.5%
201906 1
0.5%
201904 1
0.5%
201902 1
0.5%
Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
Minimum2016-01-01 00:00:00
Maximum2020-01-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-08T11:19:29.604694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:29.839860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=5)

cant_procesos_adjudicado
Real number (ℝ)

High correlation 

Distinct106
Distinct (%)53.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112.65482
Minimum0
Maximum1214
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-07-08T11:19:29.933606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.8
Q17
median29
Q3111
95-th percentile582.2
Maximum1214
Range1214
Interquartile range (IQR)104

Descriptive statistics

Standard deviation209.348
Coefficient of variation (CV)1.8583137
Kurtosis10.088762
Mean112.65482
Median Absolute Deviation (MAD)26
Skewness3.0928723
Sum22193
Variance43826.584
MonotonicityNot monotonic
2025-07-08T11:19:30.027349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 14
 
7.1%
1 9
 
4.6%
2 9
 
4.6%
22 7
 
3.6%
5 7
 
3.6%
13 5
 
2.5%
8 4
 
2.0%
6 4
 
2.0%
14 4
 
2.0%
47 3
 
1.5%
Other values (96) 131
66.5%
ValueCountFrequency (%)
0 1
 
0.5%
1 9
4.6%
2 9
4.6%
3 14
7.1%
4 3
 
1.5%
5 7
3.6%
6 4
 
2.0%
7 3
 
1.5%
8 4
 
2.0%
10 2
 
1.0%
ValueCountFrequency (%)
1214 1
0.5%
1102 1
0.5%
989 1
0.5%
895 1
0.5%
889 1
0.5%
864 1
0.5%
804 1
0.5%
792 1
0.5%
649 1
0.5%
635 1
0.5%

monto_total_adjudicado
Real number (ℝ)

High correlation  Unique 

Distinct197
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5131948 × 108
Minimum0
Maximum6.9338191 × 109
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-07-08T11:19:30.121089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile528136.25
Q17242920.7
median35279444
Q31.7485866 × 108
95-th percentile7.6927122 × 108
Maximum6.9338191 × 109
Range6.9338191 × 109
Interquartile range (IQR)1.6761573 × 108

Descriptive statistics

Standard deviation7.6536902 × 108
Coefficient of variation (CV)3.0454027
Kurtosis46.09178
Mean2.5131948 × 108
Median Absolute Deviation (MAD)34090772
Skewness6.3287812
Sum4.9509937 × 1010
Variance5.8578974 × 1017
MonotonicityNot monotonic
2025-07-08T11:19:30.212217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7425067.382 1
 
0.5%
27146231.22 1
 
0.5%
6933819091 1
 
0.5%
137988939 1
 
0.5%
3182253860 1
 
0.5%
7831100.398 1
 
0.5%
1375720032 1
 
0.5%
7242920.713 1
 
0.5%
86070142.1 1
 
0.5%
64475958.4 1
 
0.5%
Other values (187) 187
94.9%
ValueCountFrequency (%)
0 1
0.5%
6872 1
0.5%
23910.30243 1
0.5%
42000 1
0.5%
84943.48 1
0.5%
100200 1
0.5%
213056.6512 1
0.5%
240550.6182 1
0.5%
386494.5 1
0.5%
508479.1785 1
0.5%
ValueCountFrequency (%)
6933819091 1
0.5%
5903229493 1
0.5%
3182253860 1
0.5%
3132430594 1
0.5%
2548401882 1
0.5%
2252733473 1
0.5%
1375720032 1
0.5%
1375349531 1
0.5%
943321354.4 1
0.5%
780426628.5 1
0.5%

antiguedad
Categorical

High correlation 

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size13.1 KiB
5.0
83 
4.0
81 
3.0
22 
2.0
 
8
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters591
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row5.0
3rd row5.0
4th row5.0
5th row5.0

Common Values

ValueCountFrequency (%)
5.0 83
42.1%
4.0 81
41.1%
3.0 22
 
11.2%
2.0 8
 
4.1%
1.0 3
 
1.5%

Length

2025-07-08T11:19:30.306231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:30.353122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
5.0 83
42.1%
4.0 81
41.1%
3.0 22
 
11.2%
2.0 8
 
4.1%
1.0 3
 
1.5%

Most occurring characters

ValueCountFrequency (%)
. 197
33.3%
0 197
33.3%
5 83
14.0%
4 81
13.7%
3 22
 
3.7%
2 8
 
1.4%
1 3
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 591
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 197
33.3%
0 197
33.3%
5 83
14.0%
4 81
13.7%
3 22
 
3.7%
2 8
 
1.4%
1 3
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 591
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 197
33.3%
0 197
33.3%
5 83
14.0%
4 81
13.7%
3 22
 
3.7%
2 8
 
1.4%
1 3
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 591
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 197
33.3%
0 197
33.3%
5 83
14.0%
4 81
13.7%
3 22
 
3.7%
2 8
 
1.4%
1 3
 
0.5%

provincia
Categorical

Imbalance 

Distinct13
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size23.5 KiB
Ciudad Autónoma de Buenos Aires
115 
Buenos Aires
54 
Santa Fe
 
7
Córdoba
 
7
San Juan
 
3
Other values (8)
 
11

Length

Max length31
Median length31
Mean length22.532995
Min length7

Characters and Unicode

Total characters4439
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)2.5%

Sample

1st rowCiudad Autónoma de Buenos Aires
2nd rowCiudad Autónoma de Buenos Aires
3rd rowCiudad Autónoma de Buenos Aires
4th rowBuenos Aires
5th rowCiudad Autónoma de Buenos Aires

Common Values

ValueCountFrequency (%)
Ciudad Autónoma de Buenos Aires 115
58.4%
Buenos Aires 54
27.4%
Santa Fe 7
 
3.6%
Córdoba 7
 
3.6%
San Juan 3
 
1.5%
Corrientes 2
 
1.0%
San Luis 2
 
1.0%
Extranjera 2
 
1.0%
Rio Negro 1
 
0.5%
La Rioja 1
 
0.5%
Other values (3) 3
 
1.5%

Length

2025-07-08T11:19:30.431233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aires 169
23.3%
buenos 169
23.3%
ciudad 115
15.8%
de 115
15.8%
autónoma 115
15.8%
santa 7
 
1.0%
fe 7
 
1.0%
córdoba 7
 
1.0%
san 5
 
0.7%
juan 3
 
0.4%
Other values (11) 14
 
1.9%

Most occurring characters

ValueCountFrequency (%)
529
11.9%
e 469
10.6%
u 406
9.1%
d 353
 
8.0%
s 343
 
7.7%
n 306
 
6.9%
o 298
 
6.7%
i 291
 
6.6%
A 284
 
6.4%
a 266
 
6.0%
Other values (22) 894
20.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4439
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
529
11.9%
e 469
10.6%
u 406
9.1%
d 353
 
8.0%
s 343
 
7.7%
n 306
 
6.9%
o 298
 
6.7%
i 291
 
6.6%
A 284
 
6.4%
a 266
 
6.0%
Other values (22) 894
20.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4439
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
529
11.9%
e 469
10.6%
u 406
9.1%
d 353
 
8.0%
s 343
 
7.7%
n 306
 
6.9%
o 298
 
6.7%
i 291
 
6.6%
A 284
 
6.4%
a 266
 
6.0%
Other values (22) 894
20.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4439
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
529
11.9%
e 469
10.6%
u 406
9.1%
d 353
 
8.0%
s 343
 
7.7%
n 306
 
6.9%
o 298
 
6.7%
i 291
 
6.6%
A 284
 
6.4%
a 266
 
6.0%
Other values (22) 894
20.1%

cant_socios
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4619289
Minimum0
Maximum5
Zeros45
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-07-08T11:19:30.478690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3.2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1270434
Coefficient of variation (CV)0.77092901
Kurtosis0.42303727
Mean1.4619289
Median Absolute Deviation (MAD)1
Skewness0.61412749
Sum288
Variance1.2702269
MonotonicityNot monotonic
2025-07-08T11:19:30.541903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 71
36.0%
1 55
27.9%
0 45
22.8%
3 16
 
8.1%
4 7
 
3.6%
5 3
 
1.5%
ValueCountFrequency (%)
0 45
22.8%
1 55
27.9%
2 71
36.0%
3 16
 
8.1%
4 7
 
3.6%
5 3
 
1.5%
ValueCountFrequency (%)
5 3
 
1.5%
4 7
 
3.6%
3 16
 
8.1%
2 71
36.0%
1 55
27.9%
0 45
22.8%
Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size13.1 KiB
1.0
129 
0.0
58 
2.0
 
9
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters591
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 129
65.5%
0.0 58
29.4%
2.0 9
 
4.6%
3.0 1
 
0.5%

Length

2025-07-08T11:19:30.604397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:30.651277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 129
65.5%
0.0 58
29.4%
2.0 9
 
4.6%
3.0 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 255
43.1%
. 197
33.3%
1 129
21.8%
2 9
 
1.5%
3 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 591
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 255
43.1%
. 197
33.3%
1 129
21.8%
2 9
 
1.5%
3 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 591
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 255
43.1%
. 197
33.3%
1 129
21.8%
2 9
 
1.5%
3 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 591
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 255
43.1%
. 197
33.3%
1 129
21.8%
2 9
 
1.5%
3 1
 
0.2%

cant_suspensiones
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.93908629
Minimum0
Maximum7
Zeros107
Zeros (%)54.3%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-07-08T11:19:30.713764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile3
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2882298
Coefficient of variation (CV)1.3717906
Kurtosis3.8770301
Mean0.93908629
Median Absolute Deviation (MAD)0
Skewness1.7202047
Sum185
Variance1.6595359
MonotonicityNot monotonic
2025-07-08T11:19:30.760638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 107
54.3%
2 48
24.4%
1 27
 
13.7%
3 6
 
3.0%
4 5
 
2.5%
6 2
 
1.0%
5 1
 
0.5%
7 1
 
0.5%
ValueCountFrequency (%)
0 107
54.3%
1 27
 
13.7%
2 48
24.4%
3 6
 
3.0%
4 5
 
2.5%
5 1
 
0.5%
6 2
 
1.0%
7 1
 
0.5%
ValueCountFrequency (%)
7 1
 
0.5%
6 2
 
1.0%
5 1
 
0.5%
4 5
 
2.5%
3 6
 
3.0%
2 48
24.4%
1 27
 
13.7%
0 107
54.3%

cant_antecedentes
Real number (ℝ)

High correlation  Zeros 

Distinct9
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7969543
Minimum0
Maximum8
Zeros3
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-07-08T11:19:30.823130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2286507
Coefficient of variation (CV)0.68374064
Kurtosis5.8694514
Mean1.7969543
Median Absolute Deviation (MAD)0
Skewness2.1617355
Sum354
Variance1.5095825
MonotonicityNot monotonic
2025-07-08T11:19:30.885548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 100
50.8%
2 62
31.5%
3 15
 
7.6%
4 8
 
4.1%
5 4
 
2.0%
6 3
 
1.5%
0 3
 
1.5%
7 1
 
0.5%
8 1
 
0.5%
ValueCountFrequency (%)
0 3
 
1.5%
1 100
50.8%
2 62
31.5%
3 15
 
7.6%
4 8
 
4.1%
5 4
 
2.0%
6 3
 
1.5%
7 1
 
0.5%
8 1
 
0.5%
ValueCountFrequency (%)
8 1
 
0.5%
7 1
 
0.5%
6 3
 
1.5%
5 4
 
2.0%
4 8
 
4.1%
3 15
 
7.6%
2 62
31.5%
1 100
50.8%
0 3
 
1.5%

cant_Apoderado
Categorical

High correlation 

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size13.1 KiB
1.0
117 
0.0
56 
2.0
17 
3.0
 
4
4.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters591
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 117
59.4%
0.0 56
28.4%
2.0 17
 
8.6%
3.0 4
 
2.0%
4.0 3
 
1.5%

Length

2025-07-08T11:19:30.948042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:31.010534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 117
59.4%
0.0 56
28.4%
2.0 17
 
8.6%
3.0 4
 
2.0%
4.0 3
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 253
42.8%
. 197
33.3%
1 117
19.8%
2 17
 
2.9%
3 4
 
0.7%
4 3
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 591
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 253
42.8%
. 197
33.3%
1 117
19.8%
2 17
 
2.9%
3 4
 
0.7%
4 3
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 591
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 253
42.8%
. 197
33.3%
1 117
19.8%
2 17
 
2.9%
3 4
 
0.7%
4 3
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 591
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 253
42.8%
. 197
33.3%
1 117
19.8%
2 17
 
2.9%
3 4
 
0.7%
4 3
 
0.5%

cant_representante
Categorical

High correlation 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size13.1 KiB
0.0
102 
1.0
81 
2.0
13 
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters591
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row2.0

Common Values

ValueCountFrequency (%)
0.0 102
51.8%
1.0 81
41.1%
2.0 13
 
6.6%
3.0 1
 
0.5%

Length

2025-07-08T11:19:31.073027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:31.104353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 102
51.8%
1.0 81
41.1%
2.0 13
 
6.6%
3.0 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 299
50.6%
. 197
33.3%
1 81
 
13.7%
2 13
 
2.2%
3 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 591
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 299
50.6%
. 197
33.3%
1 81
 
13.7%
2 13
 
2.2%
3 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 591
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 299
50.6%
. 197
33.3%
1 81
 
13.7%
2 13
 
2.2%
3 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 591
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 299
50.6%
. 197
33.3%
1 81
 
13.7%
2 13
 
2.2%
3 1
 
0.2%

cant_autenticado
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size13.1 KiB
1.0
164 
2.0
28 
3.0
 
3
4.0
 
1
5.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters591
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 164
83.2%
2.0 28
 
14.2%
3.0 3
 
1.5%
4.0 1
 
0.5%
5.0 1
 
0.5%

Length

2025-07-08T11:19:31.178077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:31.213887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 164
83.2%
2.0 28
 
14.2%
3.0 3
 
1.5%
4.0 1
 
0.5%
5.0 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
. 197
33.3%
0 197
33.3%
1 164
27.7%
2 28
 
4.7%
3 3
 
0.5%
4 1
 
0.2%
5 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 591
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 197
33.3%
0 197
33.3%
1 164
27.7%
2 28
 
4.7%
3 3
 
0.5%
4 1
 
0.2%
5 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 591
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 197
33.3%
0 197
33.3%
1 164
27.7%
2 28
 
4.7%
3 3
 
0.5%
4 1
 
0.2%
5 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 591
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 197
33.3%
0 197
33.3%
1 164
27.7%
2 28
 
4.7%
3 3
 
0.5%
4 1
 
0.2%
5 1
 
0.2%

cant_noAutenticado
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size13.1 KiB
0.0
159 
1.0
31 
2.0
 
5
3.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters591
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
0.0 159
80.7%
1.0 31
 
15.7%
2.0 5
 
2.5%
3.0 2
 
1.0%

Length

2025-07-08T11:19:31.278504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:31.325368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 159
80.7%
1.0 31
 
15.7%
2.0 5
 
2.5%
3.0 2
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 356
60.2%
. 197
33.3%
1 31
 
5.2%
2 5
 
0.8%
3 2
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 591
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 356
60.2%
. 197
33.3%
1 31
 
5.2%
2 5
 
0.8%
3 2
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 591
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 356
60.2%
. 197
33.3%
1 31
 
5.2%
2 5
 
0.8%
3 2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 591
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 356
60.2%
. 197
33.3%
1 31
 
5.2%
2 5
 
0.8%
3 2
 
0.3%

cant_sinMontoLimite
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4111675
Minimum0
Maximum5
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-07-08T11:19:31.372237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.76829865
Coefficient of variation (CV)0.54444185
Kurtosis6.1346082
Mean1.4111675
Median Absolute Deviation (MAD)0
Skewness2.2798528
Sum278
Variance0.59028281
MonotonicityNot monotonic
2025-07-08T11:19:31.434740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 136
69.0%
2 47
 
23.9%
3 6
 
3.0%
4 5
 
2.5%
5 2
 
1.0%
0 1
 
0.5%
ValueCountFrequency (%)
0 1
 
0.5%
1 136
69.0%
2 47
 
23.9%
3 6
 
3.0%
4 5
 
2.5%
5 2
 
1.0%
ValueCountFrequency (%)
5 2
 
1.0%
4 5
 
2.5%
3 6
 
3.0%
2 47
 
23.9%
1 136
69.0%
0 1
 
0.5%

cant_MontoLimite
Categorical

Imbalance 

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size13.1 KiB
0.0
191 
1.0
 
5
2.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters591
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 191
97.0%
1.0 5
 
2.5%
2.0 1
 
0.5%

Length

2025-07-08T11:19:31.514304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:31.545560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 191
97.0%
1.0 5
 
2.5%
2.0 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 388
65.7%
. 197
33.3%
1 5
 
0.8%
2 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 591
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 388
65.7%
. 197
33.3%
1 5
 
0.8%
2 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 591
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 388
65.7%
. 197
33.3%
1 5
 
0.8%
2 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 591
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 388
65.7%
. 197
33.3%
1 5
 
0.8%
2 1
 
0.2%

total_articulos_provee
Real number (ℝ)

Distinct146
Distinct (%)74.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean416.82234
Minimum1
Maximum6661
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2025-07-08T11:19:31.623601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q125
median77
Q3329
95-th percentile2118.8
Maximum6661
Range6660
Interquartile range (IQR)304

Descriptive statistics

Standard deviation983.21762
Coefficient of variation (CV)2.358841
Kurtosis19.241767
Mean416.82234
Median Absolute Deviation (MAD)69
Skewness4.1744615
Sum82114
Variance966716.88
MonotonicityNot monotonic
2025-07-08T11:19:31.717415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 7
 
3.6%
31 5
 
2.5%
5 5
 
2.5%
3 4
 
2.0%
47 4
 
2.0%
30 3
 
1.5%
37 3
 
1.5%
16 3
 
1.5%
46 3
 
1.5%
105 3
 
1.5%
Other values (136) 157
79.7%
ValueCountFrequency (%)
1 7
3.6%
2 1
 
0.5%
3 4
2.0%
4 2
 
1.0%
5 5
2.5%
6 3
1.5%
7 2
 
1.0%
8 1
 
0.5%
9 3
1.5%
10 1
 
0.5%
ValueCountFrequency (%)
6661 1
0.5%
6064 1
0.5%
5765 1
0.5%
3956 1
0.5%
3686 1
0.5%
3605 1
0.5%
3387 1
0.5%
3210 1
0.5%
3109 1
0.5%
2566 1
0.5%

dmonto_total_adjudicado
Categorical

High correlation 

Distinct20
Distinct (%)10.2%
Missing1
Missing (%)0.5%
Memory size17.6 KiB
(222964579.98, 46172150151.0]
43 
(89439449.702, 222964579.98]
31 
(46718747.516, 89439449.702]
21 
(19975532.58, 30451916.51]
17 
(6702697.888, 9424898.401]
14 
Other values (15)
70 

Length

Max length29
Median length28
Mean length26.693878
Min length19

Characters and Unicode

Total characters5232
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)1.5%

Sample

1st row(6702697.888, 9424898.401]
2nd row(19975532.58, 30451916.51]
3rd row(222964579.98, 46172150151.0]
4th row(89439449.702, 222964579.98]
5th row(222964579.98, 46172150151.0]

Common Values

ValueCountFrequency (%)
(222964579.98, 46172150151.0] 43
21.8%
(89439449.702, 222964579.98] 31
15.7%
(46718747.516, 89439449.702] 21
10.7%
(19975532.58, 30451916.51] 17
 
8.6%
(6702697.888, 9424898.401] 14
 
7.1%
(13557176.81, 19975532.58] 11
 
5.6%
(30451916.51, 46718747.516] 7
 
3.6%
(9424898.401, 13557176.81] 7
 
3.6%
(2483085.385, 3396600.0] 7
 
3.6%
(890758.9, 1302657.558] 7
 
3.6%
Other values (10) 31
15.7%

Length

2025-07-08T11:19:31.795455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
222964579.98 74
18.9%
89439449.702 52
13.3%
46172150151.0 43
11.0%
46718747.516 28
 
7.1%
19975532.58 28
 
7.1%
30451916.51 24
 
6.1%
9424898.401 21
 
5.4%
6702697.888 20
 
5.1%
13557176.81 18
 
4.6%
2483085.385 13
 
3.3%
Other values (11) 71
18.1%

Most occurring characters

ValueCountFrequency (%)
9 589
11.3%
1 471
9.0%
4 445
8.5%
7 440
8.4%
5 437
8.4%
2 433
8.3%
. 392
 
7.5%
8 378
 
7.2%
0 308
 
5.9%
6 303
 
5.8%
Other values (6) 1036
19.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5232
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 589
11.3%
1 471
9.0%
4 445
8.5%
7 440
8.4%
5 437
8.4%
2 433
8.3%
. 392
 
7.5%
8 378
 
7.2%
0 308
 
5.9%
6 303
 
5.8%
Other values (6) 1036
19.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5232
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 589
11.3%
1 471
9.0%
4 445
8.5%
7 440
8.4%
5 437
8.4%
2 433
8.3%
. 392
 
7.5%
8 378
 
7.2%
0 308
 
5.9%
6 303
 
5.8%
Other values (6) 1036
19.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5232
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 589
11.3%
1 471
9.0%
4 445
8.5%
7 440
8.4%
5 437
8.4%
2 433
8.3%
. 392
 
7.5%
8 378
 
7.2%
0 308
 
5.9%
6 303
 
5.8%
Other values (6) 1036
19.8%
Distinct10
Distinct (%)5.1%
Missing1
Missing (%)0.5%
Memory size14.9 KiB
(39.0, 1214.0]
90 
(19.0, 39.0]
28 
(0.999, 2.0]
18 
(12.0, 19.0]
18 
(2.0, 3.0]
14 
Other values (5)
28 

Length

Max length14
Median length12
Mean length12.52551
Min length10

Characters and Unicode

Total characters2455
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(19.0, 39.0]
2nd row(39.0, 1214.0]
3rd row(39.0, 1214.0]
4th row(39.0, 1214.0]
5th row(39.0, 1214.0]

Common Values

ValueCountFrequency (%)
(39.0, 1214.0] 90
45.7%
(19.0, 39.0] 28
 
14.2%
(0.999, 2.0] 18
 
9.1%
(12.0, 19.0] 18
 
9.1%
(2.0, 3.0] 14
 
7.1%
(8.0, 12.0] 7
 
3.6%
(4.0, 5.0] 7
 
3.6%
(6.0, 8.0] 7
 
3.6%
(5.0, 6.0] 4
 
2.0%
(3.0, 4.0] 3
 
1.5%
(Missing) 1
 
0.5%

Length

2025-07-08T11:19:31.873573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:31.951689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
39.0 118
30.1%
1214.0 90
23.0%
19.0 46
 
11.7%
2.0 32
 
8.2%
12.0 25
 
6.4%
0.999 18
 
4.6%
3.0 17
 
4.3%
8.0 14
 
3.6%
5.0 11
 
2.8%
6.0 11
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 392
16.0%
. 392
16.0%
1 251
10.2%
9 218
8.9%
, 196
8.0%
( 196
8.0%
] 196
8.0%
196
8.0%
2 147
 
6.0%
3 135
 
5.5%
Other values (4) 136
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2455
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 392
16.0%
. 392
16.0%
1 251
10.2%
9 218
8.9%
, 196
8.0%
( 196
8.0%
] 196
8.0%
196
8.0%
2 147
 
6.0%
3 135
 
5.5%
Other values (4) 136
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2455
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 392
16.0%
. 392
16.0%
1 251
10.2%
9 218
8.9%
, 196
8.0%
( 196
8.0%
] 196
8.0%
196
8.0%
2 147
 
6.0%
3 135
 
5.5%
Other values (4) 136
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2455
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 392
16.0%
. 392
16.0%
1 251
10.2%
9 218
8.9%
, 196
8.0%
( 196
8.0%
] 196
8.0%
196
8.0%
2 147
 
6.0%
3 135
 
5.5%
Other values (4) 136
 
5.5%
Distinct15
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size15.0 KiB
(345.0, 6993.0]
48 
(161.0, 345.0]
21 
(97.6, 161.0]
21 
(58.0, 97.6]
20 
(40.0, 58.0]
18 
Other values (10)
69 

Length

Max length15
Median length14
Mean length12.852792
Min length10

Characters and Unicode

Total characters2532
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(97.6, 161.0]
2nd row(97.6, 161.0]
3rd row(21.0, 29.0]
4th row(161.0, 345.0]
5th row(161.0, 345.0]

Common Values

ValueCountFrequency (%)
(345.0, 6993.0] 48
24.4%
(161.0, 345.0] 21
10.7%
(97.6, 161.0] 21
10.7%
(58.0, 97.6] 20
10.2%
(40.0, 58.0] 18
 
9.1%
(29.0, 40.0] 14
 
7.1%
(21.0, 29.0] 10
 
5.1%
(4.0, 6.0] 8
 
4.1%
(0.999, 2.0] 8
 
4.1%
(15.0, 21.0] 8
 
4.1%
Other values (5) 21
10.7%

Length

2025-07-08T11:19:32.045430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
345.0 69
17.5%
6993.0 48
12.2%
161.0 42
10.7%
97.6 41
10.4%
58.0 38
9.6%
40.0 32
8.1%
29.0 24
 
6.1%
21.0 18
 
4.6%
15.0 15
 
3.8%
2.0 12
 
3.0%
Other values (6) 55
14.0%

Most occurring characters

ValueCountFrequency (%)
. 394
15.6%
0 385
15.2%
( 197
7.8%
, 197
7.8%
197
7.8%
] 197
7.8%
9 185
7.3%
6 142
 
5.6%
1 141
 
5.6%
3 123
 
4.9%
Other values (5) 374
14.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2532
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 394
15.6%
0 385
15.2%
( 197
7.8%
, 197
7.8%
197
7.8%
] 197
7.8%
9 185
7.3%
6 142
 
5.6%
1 141
 
5.6%
3 123
 
4.9%
Other values (5) 374
14.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2532
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 394
15.6%
0 385
15.2%
( 197
7.8%
, 197
7.8%
197
7.8%
] 197
7.8%
9 185
7.3%
6 142
 
5.6%
1 141
 
5.6%
3 123
 
4.9%
Other values (5) 374
14.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2532
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 394
15.6%
0 385
15.2%
( 197
7.8%
, 197
7.8%
197
7.8%
] 197
7.8%
9 185
7.3%
6 142
 
5.6%
1 141
 
5.6%
3 123
 
4.9%
Other values (5) 374
14.8%

cluster_k5
Categorical

Constant 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
2
197 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters197
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 197
100.0%

Length

2025-07-08T11:19:32.107922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:32.139168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 197
100.0%

Most occurring characters

ValueCountFrequency (%)
2 197
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 197
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 197
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 197
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 197
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 197
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 197
100.0%

Interactions

2025-07-08T11:19:27.229849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:23.003395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:23.929743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:24.464621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:24.995520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:25.496709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:26.179712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:26.728953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:27.295588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:23.451576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:24.011800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:24.529366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:25.062042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:25.584630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:26.257395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:26.798450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:27.362213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:23.514066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:24.076488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:24.615052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:25.131392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:25.645517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:26.328136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:26.861969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:27.428834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:23.592184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:24.131647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:24.682076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:25.198380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:25.722324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:26.395187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:26.911931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:27.495563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:23.654688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:24.196212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:24.729748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:25.262980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:25.784332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:26.446179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:26.979957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:27.563167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:23.717185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:24.264544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:24.795542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:25.314590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:25.851166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:26.527651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:27.045425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:27.629782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:23.795288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:24.331077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:24.878614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:25.380413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:25.918873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:26.579941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:27.095506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:27.679821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:23.866466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:24.397801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:24.928935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:25.445509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:26.116306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:26.646450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:27.169235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-08T11:19:32.196632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
EstadoTipoSocietarioantiguedadcant_Apoderadocant_MontoLimitecant_antecedentescant_apercibimientoscant_autenticadocant_noAutenticadocant_procesos_adjudicadocant_representantecant_sinMontoLimitecant_socioscant_suspensionesdcant_procesos_adjudicadodmonto_total_adjudicadodtotal_articulos_proveemonto_total_adjudicadoperiodo_preinscripcionprovinciatotal_articulos_provee
Estado1.0000.3630.1750.0000.0000.0000.0000.2290.0000.0000.0000.0000.0000.1060.1340.3000.1300.0000.2110.3530.000
TipoSocietario0.3631.0000.3100.1880.0000.0000.0940.0000.0000.0000.2980.0350.4530.0000.0000.1560.2800.4320.2670.3890.000
antiguedad0.1750.3101.0000.0150.0000.0740.0000.0000.0390.0000.0000.0000.0490.0440.0000.1210.1190.0000.9970.3270.000
cant_Apoderado0.0000.1880.0151.0000.1710.0000.0000.4500.4160.0000.3820.5450.2160.0000.0890.0000.1510.0000.0000.0000.000
cant_MontoLimite0.0000.0000.0000.1711.0000.0000.0000.0000.1490.0000.0000.2740.0000.2240.1730.0740.0000.0000.0000.0000.000
cant_antecedentes0.0000.0000.0740.0000.0001.0000.4220.0000.000-0.0730.121-0.103-0.2300.8190.0000.0000.000-0.1870.1340.2460.129
cant_apercibimientos0.0000.0940.0000.0000.0000.4221.0000.0600.0000.0000.0910.0410.0800.3840.1540.0000.0410.0000.0000.0000.082
cant_autenticado0.2290.0000.0000.4500.0000.0000.0601.0000.0000.0000.3760.6210.1090.0000.0000.1470.0000.0000.0000.1850.000
cant_noAutenticado0.0000.0000.0390.4160.1490.0000.0000.0001.0000.0000.1610.5700.2010.0000.0000.0000.2230.1010.0000.0000.000
cant_procesos_adjudicado0.0000.0000.0000.0000.000-0.0730.0000.0000.0001.0000.0000.042-0.055-0.1080.0000.0000.0000.668-0.2110.0000.431
cant_representante0.0000.2980.0000.3820.0000.1210.0910.3760.1610.0001.0000.3650.3010.1810.2100.5380.1230.0000.0000.0000.109
cant_sinMontoLimite0.0000.0350.0000.5450.274-0.1030.0410.6210.5700.0420.3651.0000.297-0.1640.0000.0000.1500.120-0.1460.000-0.118
cant_socios0.0000.4530.0490.2160.000-0.2300.0800.1090.201-0.0550.3010.2971.000-0.2560.1050.0000.1430.262-0.2860.000-0.057
cant_suspensiones0.1060.0000.0440.0000.2240.8190.3840.0000.000-0.1080.181-0.164-0.2561.0000.1070.1780.000-0.2050.1460.2480.145
dcant_procesos_adjudicado0.1340.0000.0000.0890.1730.0000.1540.0000.0000.0000.2100.0000.1050.1071.0000.3430.1910.0000.0000.1410.000
dmonto_total_adjudicado0.3000.1560.1210.0000.0740.0000.0000.1470.0000.0000.5380.0000.0000.1780.3431.0000.0210.0000.0730.1570.000
dtotal_articulos_provee0.1300.2800.1190.1510.0000.0000.0410.0000.2230.0000.1230.1500.1430.0000.1910.0211.0000.2680.0970.1670.000
monto_total_adjudicado0.0000.4320.0000.0000.000-0.1870.0000.0000.1010.6680.0000.1200.262-0.2050.0000.0000.2681.000-0.2630.0000.193
periodo_preinscripcion0.2110.2670.9970.0000.0000.1340.0000.0000.000-0.2110.000-0.146-0.2860.1460.0000.0730.097-0.2631.0000.322-0.024
provincia0.3530.3890.3270.0000.0000.2460.0000.1850.0000.0000.0000.0000.0000.2480.1410.1570.1670.0000.3221.0000.000
total_articulos_provee0.0000.0000.0000.0000.0000.1290.0820.0000.0000.4310.109-0.118-0.0570.1450.0000.0000.0000.193-0.0240.0001.000

Missing values

2025-07-08T11:19:27.813491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-08T11:19:27.988963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-08T11:19:28.118582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CUITNombreFechaPreinscripcionEstadoTipoSocietarioperiodo_preinscripcionanio_preinscripcioncant_procesos_adjudicadomonto_total_adjudicadoantiguedadprovinciacant_socioscant_apercibimientoscant_suspensionescant_antecedentescant_Apoderadocant_representantecant_autenticadocant_noAutenticadocant_sinMontoLimitecant_MontoLimitetotal_articulos_proveedmonto_total_adjudicadodcant_procesos_adjudicadodtotal_articulos_proveecluster_k5
230711500363LICICOM S.R.L.15/09/2016InscriptoSociedad Responsabilidad Limitada201609201622.07.425067e+065.0Ciudad Autónoma de Buenos Aires2.01.02.04.02.01.02.01.03.00.0105.0(6702697.888, 9424898.401](19.0, 39.0](97.6, 161.0]2
2330590151013VIDITEC S.A..22/07/2016InscriptoSociedad Anónima201607201647.02.714623e+075.0Ciudad Autónoma de Buenos Aires5.01.00.01.01.00.01.00.01.00.0113.0(19975532.58, 30451916.51](39.0, 1214.0](97.6, 161.0]2
2430678561165NACION SEGUROS S.A.15/11/2016InscriptoSociedad Anónima20161120161102.06.933819e+095.0Ciudad Autónoma de Buenos Aires5.00.00.00.01.00.01.00.01.00.026.0(222964579.98, 46172150151.0](39.0, 1214.0](21.0, 29.0]2
2930591267759ERRE-DE SRL02/08/2016InscriptoSociedad Responsabilidad Limitada2016082016340.01.379889e+085.0Buenos Aires2.01.00.01.03.00.01.02.02.01.0226.0(89439449.702, 222964579.98](39.0, 1214.0](161.0, 345.0]2
3530702024834Datastar Argentina S.A.09/09/2016InscriptoSociedad Anónima2016092016111.03.182254e+095.0Ciudad Autónoma de Buenos Aires2.01.00.01.02.02.02.02.04.00.0207.0(222964579.98, 46172150151.0](39.0, 1214.0](161.0, 345.0]2
3930623295946LAVIERI HNOS DE LAVIERI SEBASTIAN GABRIEL LAVIERI ALEJANDRO CARLOS.29/09/2016InscriptoSociedades De Hecho201609201622.07.831100e+065.0Ciudad Autónoma de Buenos Aires2.01.00.01.00.02.02.00.02.00.037.0(6702697.888, 9424898.401](19.0, 39.0](29.0, 40.0]2
6030673249902LOMAS DEL SOL SRL21/10/2016SuspendidoSociedad Responsabilidad Limitada201610201627.01.375720e+095.0San Juan2.00.04.04.01.00.01.00.01.00.0602.0(222964579.98, 46172150151.0](19.0, 39.0](345.0, 6993.0]2
8530714236888NANOTEC S.R.L.30/09/2016InscriptoSociedad Responsabilidad Limitada201609201612.07.242921e+065.0Ciudad Autónoma de Buenos Aires2.01.00.01.01.00.01.00.01.00.0160.0(6702697.888, 9424898.401](8.0, 12.0](97.6, 161.0]2
8930710362218Licenciasonline SA13/09/2016InscriptoSociedad Anónima20160920161.08.607014e+075.0Ciudad Autónoma de Buenos Aires4.01.00.01.01.01.01.01.02.00.03.0(46718747.516, 89439449.702](0.999, 2.0](2.0, 3.0]2
10330710828608INFORMÁTICA PALMAR SRL04/10/2016InscriptoSociedad Responsabilidad Limitada2016102016143.06.447596e+075.0Ciudad Autónoma de Buenos Aires2.01.00.01.00.02.01.01.02.00.037.0(46718747.516, 89439449.702](39.0, 1214.0](29.0, 40.0]2
CUITNombreFechaPreinscripcionEstadoTipoSocietarioperiodo_preinscripcionanio_preinscripcioncant_procesos_adjudicadomonto_total_adjudicadoantiguedadprovinciacant_socioscant_apercibimientoscant_suspensionescant_antecedentescant_Apoderadocant_representantecant_autenticadocant_noAutenticadocant_sinMontoLimitecant_MontoLimitetotal_articulos_proveedmonto_total_adjudicadodcant_procesos_adjudicadodtotal_articulos_proveecluster_k5
689420271824638Albano Equipamientos24/08/2018InscriptoPersona Física20180820183.02.859164e+063.0Buenos Aires0.01.00.01.01.00.01.00.01.00.045.0(2483085.385, 3396600.0](2.0, 3.0](40.0, 58.0]2
704630711958181DEL BIANCO ELECTRONICA S.A.13/03/2017InscriptoSociedad Anónima20170320173.02.405506e+054.0Ciudad Autónoma de Buenos Aires3.01.02.03.00.03.03.00.03.00.0319.0(224078.198, 377939.298](2.0, 3.0](161.0, 345.0]2
738730708351187AR TECHNOLOGY S.R.L.28/06/2018InscriptoSociedad Responsabilidad Limitada20180620185.08.783142e+063.0Ciudad Autónoma de Buenos Aires2.01.00.01.00.01.01.00.01.00.022.0(6702697.888, 9424898.401](4.0, 5.0](21.0, 29.0]2
815030715995979DESARROLLOS URBANOS RIO DE LA PLATA SRL16/09/2020InscriptoSociedad Responsabilidad Limitada202009202018.02.095676e+081.0Ciudad Autónoma de Buenos Aires2.01.00.01.00.01.01.00.01.00.018.0(89439449.702, 222964579.98](12.0, 19.0](15.0, 21.0]2
816530709585831SECON SECURITY CONCEPT SA11/04/2017InscriptoSociedad Anónima20170420171.01.326591e+074.0Buenos Aires2.01.00.01.01.01.01.01.01.01.0102.0(9424898.401, 13557176.81](0.999, 2.0](97.6, 161.0]2
895627181287077FABIANA SANDRA CORTES10/04/2017InscriptoPersona Física201704201713.01.682270e+074.0Buenos Aires0.01.07.08.01.00.01.00.01.00.0254.0(13557176.81, 19975532.58](12.0, 19.0](161.0, 345.0]2
8957A28006104AIRBUS DEFENCE AND SPACE S.A.21/06/2019Pre InscriptoPersona Jurídica Extranjero Sin Sucursal20190620193.08.494348e+042.0Extranjera1.01.00.01.02.00.02.00.02.00.014.0(33011.111, 104767.373](2.0, 3.0](11.0, 15.0]2
9237214349010016FARINTO S.A.05/07/2019Pre InscriptoPersona Jurídica Extranjero Sin Sucursal20190720190.00.000000e+002.0Extranjera1.01.00.01.01.00.01.00.01.00.06.0NaNNaN(4.0, 6.0]2
950430716582082BATERIAS ECOBAT S.A.S16/10/2020InscriptoOtras Formas Societarias20201020201.08.288520e+051.0San Juan1.01.00.01.00.01.01.00.01.00.01.0(599760.0, 890758.9](0.999, 2.0](0.999, 2.0]2
978330707835563SUTEL S.R.L.25/08/2016InscriptoSociedad Responsabilidad Limitada20160820162.03.889114e+065.0Buenos Aires2.00.02.02.00.01.01.00.01.00.0329.0(3396600.0, 4727330.113](0.999, 2.0](161.0, 345.0]2